Online inquiry information processing method and system based on big data

Abstract

The invention discloses an online inquiry information processing method and system based on big data, and relates to the technical field of online inquiry. The method comprises the following steps: acquiring illness state information and operation information submitted by a patient, arranging effective information in the illness state information and recording the effective information as patient information; obtaining corresponding doctor diagnosis information according to the patient information, and converting the doctor diagnosis information to obtain basic information; acquiring online conditions of doctors, matching to obtain text content contents, and adjusting and generating primary information according to the text content contents and the basic information; acquiring a historical browsing record of the patient, obtaining an emphasis of the patient concerning the information, and adjusting and generating intermediate information according to the emphasis and the primary information; analyzing according to the patient information to obtain patient emotion, adjusting and generating advanced information according to the patient emotion and the primary information, and pushing the advanced information to the patient. According to the invention, the convenience and accuracy of online inquiry based on big data are improved.

Description

Technical Field

[0001] The present application relates to the technical field of online medical consultation, and in particular to a method and system for processing online medical consultation information based on big data. Background Art

[0002] Online consultation refers to medical consultation activities conducted by doctors and patients through Internet technology, providing relevant consulting services to promote the patient's diagnosis and treatment process. With the continuous development and popularization of information technology, online consultation is gradually becoming a new type of medical service mode, playing an important role in alleviating the uneven distribution of medical resources and improving the efficiency of medical services. At present, there is a problem of poor communication in online consultation. On the one hand, it is difficult for patients to understand the professional answers of doctors and to accurately grasp the severity of the disease. On the other hand, the information provided by patients may not be very accurate, and it is difficult for doctors to distinguish, which can easily lead to misdiagnosis and bring inconvenience to online consultation. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for processing online medical consultation information based on big data to solve the problems raised in the above background technology.

[0004] In the first aspect, the present application provides a method for processing online medical consultation information based on big data, which adopts the following technical solution:

[0005] Obtain the medical condition information and operation information submitted by the patient, organize the valid information in the medical condition information and record it as patient information;

[0006] Obtain corresponding doctor's diagnosis information according to the patient information, and convert the doctor's diagnosis information to obtain basic information;

[0007] Obtain the online status of doctors, match the text content according to the online status of doctors, and adjust and generate primary information according to the text content and basic information;

[0008] Obtain the patient's historical browsing history, obtain the focus of the patient's attention based on the historical browsing history, and adjust and generate intermediate information based on the focus and primary information;

[0009] The patient's emotions are obtained based on the patient information analysis, and advanced information is generated based on the patient's emotions and primary information, and the advanced information is pushed to the patient.

[0010] Preferably, the step of obtaining the condition information and operation information submitted by the patient, collating the valid information in the condition information and recording it as the patient information, specifically comprises:

[0011] Obtain the medical condition information and operation information submitted by the patient, filter out the medical condition information that is completely irrelevant to the condition, and obtain the basic information;

[0012] Find the corresponding disease type according to the basic information and record it as the basic disease type;

[0013] Obtain the average timeliness of basic information corresponding to basic disease types, extract the real-time timeliness of basic information, filter out basic information whose real-time timeliness exceeds the average timeliness and form effective information;

[0014] Determine whether the valid information is image information. If the valid information is image information, extract the clarity of the image information as the information fuzziness;

[0015] If the valid information is not image information, the text information of the valid information is obtained and recorded as the patient text information, and the information ambiguity is obtained according to the patient text information;

[0016] Set the fuzziness threshold, filter out the valid information that exceeds the fuzziness threshold and record it as patient information.

[0017] Preferably, if the valid information is not image information, the steps of obtaining text information of the valid information and recording it as patient text information, and obtaining the information ambiguity according to the patient text information are specifically as follows:

[0018] If the valid information is not image information, the valid information is extracted and converted into text information and recorded as patient text information;

[0019] Count the average operation time AP of all information in the patient operation information;

[0020] Acquire operation information corresponding to the patient text information, wherein the operation information includes operation duration AC and operation modification frequency AX;

[0021] Extract the descriptive words of the patient's text information, sort out the proportion of fuzzy words in the descriptive words and record it as the fuzzy word proportion AM;

[0022] Obtain the criticality AG of the patient's text information in the basic disease type, according to the fuzzy correlation function The information ambiguity AH is calculated, where a1, a2, a3, and a4 are proportional factors and are greater than 0.

[0023] Preferably, the step of obtaining corresponding doctor's diagnosis information according to the patient information and converting the doctor's diagnosis information to obtain basic information is specifically as follows:

[0024] Obtaining corresponding doctor's diagnosis information according to the patient information, and determining whether the doctor's diagnosis information has corresponding image information;

[0025] If the doctor's diagnosis information has corresponding image information, the human body part displayed in the image information is extracted and recorded as the displayed part;

[0026] It is determined whether the patient can directly observe the display part. If the patient can directly observe the display part, the doctor's diagnosis information is converted into image information and recorded as basic information;

[0027] If the patient cannot directly observe the display part, the doctor's diagnosis information is converted into text information and recorded as the doctor's text information;

[0028] If the doctor's diagnosis information has no corresponding image information, the doctor's diagnosis information is converted into text information and recorded as doctor's text information;

[0029] After processing the doctor's text information, basic information is obtained.

[0030] Preferably, the step of obtaining basic information after processing the doctor's text information is specifically as follows:

[0031] Extract the disease type from the doctor's diagnosis information and record it as the reference disease type;

[0032] Determine whether the patient understands the reference condition type. If the patient understands the reference condition type, the doctor's text information is not processed to obtain basic information;

[0033] If the patient does not know the reference disease type, the patient's work experience is obtained, and the patient's work industry is extracted based on the work experience;

[0034] Match each part of the human body to each part of the work in the industry, extract the part corresponding to the reference disease type and record it as the reference part;

[0035] Find the part of the work corresponding to the reference part and record it as the reference work, obtain the workflow of the reference work and record it as the reference workflow;

[0036] The workflow of the reference part is obtained and recorded as the part workflow, and the reference workflow is matched with the part workflow one by one to obtain a process comparison table;

[0037] Extract the descriptive meaning of the doctor's text information and find the part workflow corresponding to the descriptive meaning;

[0038] According to the process comparison table, the part workflow is converted into the corresponding reference workflow, forming an example and outputting basic information.

[0039] Preferably, the step of matching various parts of the human body with various parts of work in the work industry one by one, extracting parts corresponding to the reference disease type and recording them as reference parts, specifically comprises:

[0040] Obtain the main functions of each part of the human body, and obtain the higher-level functional information of the parts after performing upper-level generalization processing on the main functions;

[0041] Obtain the main functions of each part of the work in the work industry, and obtain the upper-level function information of some parts of the work after summarizing the main functions;

[0042] Find the partial work whose upper function information is consistent with the upper function information of the part and obtain the pending partial work;

[0043] Obtain the criticality of the parts in the operation of the human body, and find out the importance of the pending parts of work in the work industry;

[0044] Find the pending partial work corresponding to the criticality and importance to obtain the partial work corresponding to the human body part;

[0045] Find the corresponding parts of the work for all parts, and achieve a one-to-one correspondence between various parts of the human body and various parts of the work in the work industry.

[0046] Preferably, the steps of obtaining the online status of doctors, matching the text content according to the online status of doctors, and adjusting and generating primary information according to the text content and basic information are specifically as follows:

[0047] Obtain the online status of doctors and obtain the average response interval of doctors based on the online status;

[0048] Obtain the patient's historical response records, and extract the patient's browsing speed based on the historical response records;

[0049] The text content of the basic information is obtained and recorded as the basic text content, and the patient's browsing time is calculated according to the patient's browsing speed and the basic text content;

[0050] Determine whether the browsing time is less than the average reply interval time. If the browsing time is less than the average reply interval time, calculate the difference between the browsing time and the average reply interval time.

[0051] According to the preset correlation curve between the time difference and the text content, the corresponding text content is found and recorded as the standard text content;

[0052] According to the text difference between the basic text content and the standard text content, the content corresponding to the text difference is added to the basic information to generate primary information.

[0053] Preferably, the step of adding content corresponding to the text difference between the basic text content and the standard text content to the basic information and generating the primary information is specifically as follows:

[0054] The basic information is classified according to whether the description is completely specific, and specific information and non-specific information are obtained respectively;

[0055] Obtain the amount of information in the non-specific information, sort it from small to large according to the amount of information, and obtain the information size sorting;

[0056] Sort by information size, add details to non-specific information in turn, and add and record the added text content in turn, which is recorded as the text added value;

[0057] When the text addition value exceeds the text difference value, stop adding details and form primary information.

[0058] Preferably, the steps of obtaining the patient's historical browsing records, obtaining the focus of the patient's attention information according to the historical browsing records, and adjusting and generating the intermediate information according to the focus and the primary information are specifically as follows:

[0059] Obtain the patient's browsing history and extract the patient's browsing duration on different pages;

[0060] Extract the information contents corresponding to the browsing duration respectively and obtain the information content types;

[0061] The type percentage is formed based on the ratio of the browsing dwell time corresponding to the information content type to the total browsing dwell time;

[0062] The percentage of different types of content in the primary information is adjusted according to the type percentage, and the intermediate information is generated after it is consistent with the type percentage.

[0063] In the second aspect, the present application provides a system for processing online medical consultation information based on big data, which adopts the following technical solutions:

[0064] A processing system for online medical consultation information based on big data, comprising:

[0065] The patient information module obtains the medical condition information and operation information submitted by the patient, sorts out the valid information in the medical condition information and records it as the patient information;

[0066] The basic information module obtains the corresponding doctor's diagnosis information according to the patient information, and converts the doctor's diagnosis information to obtain the basic information;

[0067] The primary information module obtains the online status of doctors, matches the text content according to the online status of doctors, and generates primary information according to the text content and basic information;

[0068] The intermediate information module obtains the patient's historical browsing records, obtains the focus of the patient's attention based on the historical browsing records, and generates intermediate information based on the focus and primary information;

[0069] The advanced information module obtains the patient's emotions based on the patient information analysis, generates advanced information based on the patient's emotions and primary information, and pushes the advanced information to the patient.

[0070] In summary, the present application includes at least one of the following beneficial technical effects:

[0071] 1. First, the patient's condition information and operation information are screened to obtain effective information useful for diagnosis. The doctor gives the corresponding diagnosis information based on the effective information, converts and processes the diagnosis information to obtain basic information, and then processes the basic information in combination with the doctor's online status, the patient's historical browsing history, and the patient's emotions, and then obtains advanced information and pushes it to the patient. By processing the natural language during the consultation process, the information pushed to the patient is easier to understand and more in line with the patient's habits. The patient obtains more accurate information that suits him or her, which reduces the communication barriers of online consultations and improves the accuracy of online consultations based on big data.

[0072] 2. Determine the display format of the consultation information based on whether the doctor's consultation information is suitable for conversion into image information. When the consultation information is displayed in text, a process comparison table is obtained based on the one-to-one correspondence between human body parts and industry jobs familiar to patients, and the corresponding information is converted into work information familiar to patients to form an example explanation, so that patients can more intuitively, clearly, quickly and easily understand their condition. By explaining the natural language in online consultations at an appropriate distance, the difficulty of consultation information is simplified, which brings convenience to patients and improves the convenience of online consultations based on big data.

[0073] 3. Determine whether to add detailed content to the basic information based on the doctor's online status. If it is necessary to add detailed content to the basic information, the amount of information of different non-specific information will be increased from the least to the most until the text difference is reached. This will increase the patient's understanding of the condition while reducing the patient's waiting time. By processing the natural language of online consultations, the patient's time and the doctor's time can be fully adjusted, time waste can be reduced, and the intelligence of online consultations based on big data can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a schematic diagram of the specific steps of an embodiment of a method for processing online medical consultation information based on big data of the present invention.

[0075] Figure 2 It is a module connection diagram of an embodiment of an online medical consultation information processing system based on big data of the present invention. DETAILED DESCRIPTION

[0076] Below is a combination of the embodiments and Figure 1-Figure 2 The present invention is further described in detail, but the embodiments of the present invention are not limited thereto.

[0077] The present invention discloses a method for processing online medical consultation information based on big data, which specifically comprises the following steps:

[0078] Step S1, obtaining the medical condition information and operation information submitted by the patient, sorting out the valid information in the medical condition information and recording it as the patient information.

[0079] Step S2, obtaining corresponding doctor's diagnosis information according to the patient information, and converting the doctor's diagnosis information to obtain basic information.

[0080] Step S3, obtaining the online status of doctors, matching the text content according to the online status of doctors, and generating primary information according to the text content and basic information adjustment.

[0081] Step S4, obtaining the patient's historical browsing records, obtaining the focus of the patient's attention information based on the historical browsing records, and adjusting and generating intermediate information based on the focus and primary information.

[0082] Step S5, obtaining the patient's emotions based on the patient information analysis, adjusting and generating advanced information based on the patient's emotions and the primary information, and pushing the advanced information to the patient.

[0083] Extract emotional expression words from patient information and analyze the patient's emotions based on the emotional expression words. If the patient is not optimistic or irritable, add some soothing words, adjust the language style, and reduce the seriousness of the language style.

[0084] In actual use, since patients and doctors cannot communicate directly face to face during online consultations, communication barriers are likely to occur. Patients cannot understand a large number of professional terms, which leads to patients being unable to fully understand their condition, thus missing the best treatment time. And if patients cannot understand, they will repeatedly ask questions about the doctor's reply, which increases the inconvenience of online consultations. At the same time, some patients cannot accurately convey their condition information, and some wrong information may cause doctors to misdiagnose. Therefore, first of all, screening patient information can make the consultation results more accurate. Secondly, converting the doctor's diagnostic information allows patients to understand the doctor's meaning more intuitively and clearly. Adjusting the diagnostic information according to the doctor's online situation can reduce the patient's waiting time. Adjusting according to the browsing history can output information that patients are more concerned about, while taking care of the patient's emotions. It improves the patient's online consultation experience and the convenience of online consultation.

[0085] The steps of obtaining the medical condition information and operation information submitted by the patient, sorting out the valid information in the medical condition information and recording it as the patient information are as follows:

[0086] Step S11, obtaining the condition information and operation information submitted by the patient, screening and removing the condition information that is completely irrelevant to the condition to obtain basic information.

[0087] According to the condition information provided by the patient, the information required for the corresponding condition information is searched and used as reference information, and then the information is filtered and removed according to the reference information. For example, if the patient has a fracture, then what he ate at noon is irrelevant to the fracture condition and should be removed.

[0088] Step S12, searching for the corresponding disease type according to the basic information and recording it as the basic disease type.

[0089] According to the disease characteristics in the disease information, the corresponding disease type can be preliminarily found. For example, if vomiting does not stop, then the digestive tract series of diseases will be taken as the basic disease type.

[0090] Step S13, obtaining the average timeliness corresponding to the basic information in the basic disease type, extracting the real-time timeliness of the basic information, screening and removing the basic information whose real-time timeliness exceeds the average timeliness, and forming effective information.

[0091] Medical condition information is time-sensitive. For example, allergies have an incubation period, which usually ranges from a few minutes to a few days. If you provide information about what you ate a month ago and it didn't have any effect, you are not providing effective information. Therefore, information that is out of time is useless and needs to be screened out.

[0092] Step S14, determining whether the valid information is image information, if the valid information is image information, extracting the clarity of the image information as the information fuzziness.

[0093] Step S15: if the valid information is not image information, the text information of the valid information is obtained and recorded as the patient text information, and the information ambiguity is obtained according to the patient text information.

[0094] Step S16, setting a fuzziness threshold, filtering out valid information that exceeds the fuzziness threshold and recording it as patient information.

[0095] In actual use, not all information provided by patients is valid, and invalid information needs to be screened out. In addition to screening out irrelevant information and outdated information, the information provided by patients is too vague and cannot help doctors make judgments. For example, if a patient has skin allergies, it is necessary to provide pictures of the patient's allergies so that the doctor can check whether the allergy is serious. However, the pictures provided by the patient are too vague, and the doctor cannot determine whether it is an allergy and the severity of the allergy. Therefore, this information is also invalid information and needs to be removed to reduce the workload of the doctor during the consultation process.

[0096] If the valid information is not image information, the text information of the valid information is obtained and recorded as the patient text information. The steps of obtaining the information ambiguity according to the patient text information are specifically as follows:

[0097] Step S151: if the valid information is not image information, the valid information is extracted and converted into text information and recorded as patient text information.

[0098] Step S152, counting the average operation time AP of all information in the patient operation information.

[0099] Step S153, obtaining operation information corresponding to the patient text information, where the operation information includes operation duration AC and operation modification frequency AX.

[0100] The greater the frequency of modification, the less certain the patient is about the information he or she has given, thus reflecting that the information is vague.

[0101] Step S154, extracting descriptive words from the patient's text information, sorting out the proportion of fuzzy words in the descriptive words and recording it as the fuzzy word proportion AM.

[0102] Pre-set fuzzy words and compare to determine whether the words are fuzzy words.

[0103] Step S155, obtaining the criticality AG of the patient's text information in the basic disease type, and using the fuzzy correlation function The information ambiguity AH is calculated, where a1, a2, a3, and a4 are proportional factors and are greater than 0.

[0104] In actual application, in addition to the blurred images, some of the information provided by the patients is also ambiguous. This information not only cannot provide help to the doctor, but also interferes with the doctor's judgment, resulting in inaccurate consultation results. Therefore, the more ambiguous information also needs to be removed. If the patient's description of the symptoms is vague and uses too many ambiguous words (such as "a little", "maybe", "seems", etc.), this indicates that the patient does not have a deep understanding of his or her condition or has inaccurate memory, which makes it impossible for the doctor to make an effective judgment. At the same time, the more time the patient spends in the operation process, the longer the patient thinks, so the memory of the information provided is not clear, and there may be wrong information. The criticality of the information is also very important. For critical information, the ambiguity will be more tolerant because it is needed in the consultation process. For example, in the process of calculating the ambiguity, the greater the criticality, the smaller the ambiguity. This means that if the criticality of information A is greater than that of information B under the same conditions, the ambiguity of the calculated information A will be smaller, which is conducive to retaining information A in the screening process, while screening clear information, reducing the loss of critical information.

[0105] The steps of obtaining corresponding doctor's diagnosis information according to patient information and converting the doctor's diagnosis information to obtain basic information are as follows:

[0106] Step S21, obtaining corresponding doctor's diagnosis information according to the patient information, and determining whether the doctor's diagnosis information has corresponding image information.

[0107] Some diagnostic information has corresponding image information. For example, if a doctor diagnoses a skin disease with a rash, the disease has a corresponding image. However, if a doctor diagnoses anemia, there is no corresponding image information.

[0108] Step S22: If the doctor's diagnosis information has corresponding image information, the human body part displayed by the image information is extracted and recorded as the displayed part.

[0109] In step S23, it is determined whether the patient can directly observe the display part. If the patient can directly observe the display part, the doctor's diagnosis information is converted into image information and recorded as basic information.

[0110] Step S24: If the patient cannot directly observe the displayed part, the doctor's diagnosis information is converted into text information and recorded as doctor's text information.

[0111] Step S25: If the doctor's diagnosis information has no corresponding image information, the doctor's diagnosis information is converted into text information and recorded as doctor's text information.

[0112] Step S26, processing the doctor's text information to obtain basic information.

[0113] In actual use, if the information has a high degree of visual elements, then converting it into a picture form can often express this information more intuitively. However, not all information can be displayed through images, so patients can clearly compare and confirm it. For example, skin allergies can be observed directly by patients. However, patients cannot directly observe problems with internal organs such as the liver and stomach, so even if image information is given, it is difficult for patients to confirm. At the same time, because the original condition of the internal organs is unknown, it is impossible to understand one's condition specifically from the image. Therefore, doctors choose the appropriate display method for diagnostic information according to the actual situation, which is conducive to patients understanding their condition more intuitively and clearly.

[0114] The steps of obtaining basic information after processing the doctor's text information are as follows:

[0115] Step S261, extracting the disease type from the doctor's diagnosis information and recording it as a reference disease type.

[0116] Step S262, determining whether the patient understands the reference condition type. If the patient understands the reference condition type, the doctor's text information is not processed to obtain basic information.

[0117] It is determined whether the patient has suffered from this type of reference condition based on the patient's medical history. If the patient has experienced the condition, it is considered that the patient understands the type of condition, otherwise it is considered that the patient does not understand the condition.

[0118] Step S263: If the patient does not know the reference disease type, the patient's work experience is obtained, and the patient's work industry is extracted based on the work experience.

[0119] Step S264, correspond each part of the human body to each part of the work in the work industry, extract the part corresponding to the reference disease type and record it as the reference part.

[0120] Step S265, searching for the part of the work corresponding to the reference part and recording it as the reference work, obtaining the workflow of the reference work and recording it as the reference workflow.

[0121] Step S266, obtaining the workflow of the reference part and recording it as the part workflow, making a one-to-one correspondence between the reference workflow and the part workflow, and obtaining a process comparison table.

[0122] Step S267, extract the descriptive meaning of the doctor's text information, and search for the part workflow corresponding to the descriptive meaning.

[0123] Step S268, converting the part workflow into a corresponding reference workflow according to the workflow comparison table, forming an example and outputting basic information.

[0124] In actual application, there are many professional terms in medicine. For patients who do not understand their condition, it is difficult to understand the meaning of these professional terms for the first time. By converting examples into areas that patients are familiar with, patients can understand the meaning more quickly and easily, and master their own knowledge. For example, the patient's job belongs to the construction industry, and the patient is familiar with the construction industry. The patient's condition is a mild muscle strain, which can be healed by rest. At this time, the muscle can be likened to the protective door of the house. Mild muscle strain is equivalent to a small gap in the protective door, but it does not affect the protection of the protective door. In this way, the patient knows that the condition is not very serious and does not need to go to the hospital. If the patient's blood vessels have problems, the blood vessels are similar to the water and electricity pipelines of the house. The water and electricity pipelines are the infrastructure in the building, responsible for transporting energy and materials such as water and electricity, and are an indispensable part. The patient knows the severity of the disease and needs to go to the hospital immediately. If the patient does not understand the condition, it is easy to miss the best time for treatment.

[0125] The steps of matching various parts of the human body with various parts of work in the work industry, extracting the parts corresponding to the reference disease types and recording them as reference parts are as follows:

[0126] Step S2641, obtaining the main functions of each part of the human body, and performing superordinate summarization processing on the main functions to obtain superordinate functional information of the parts.

[0127] Step S2642, obtain the main functions of each part of the work in the work industry, and obtain the upper-level function information of the part of the work after summarizing the main functions.

[0128] Step S2643, searching for partial work whose superordinate function information is consistent with the superordinate function information of the part and obtaining the pending partial work.

[0129] Step S2644, obtain the criticality of the parts in the operation of the human body, and find out the importance of the pending parts of the work in the work industry.

[0130] The criticality is determined by the survival rate and convenience of life of a human body without a part, and the importance is determined by the work efficiency after part of the work disappears.

[0131] Step S2645, searching for pending partial work corresponding to the criticality and importance to obtain partial work corresponding to the human body part.

[0132] Find the keyword with the closest degree and importance as the corresponding part of the work.

[0133] Step S2646, searching for the parts of work corresponding to all parts of the body, so as to achieve a one-to-one correspondence between various parts of the human body and various parts of work in the work industry.

[0134] In actual application, matching various parts of the human body with various parts of the work is actually a complex process, which involves understanding of human anatomy, physiological functions and the nature of work. The functions of human organs are summarized in a higher level, and the work content is summarized in a higher level, so as to select the same functions for correspondence. The brain is equivalent to the "decision-making center" at work, responsible for receiving information, processing and analyzing, making decisions and issuing instructions. Different areas of the brain are responsible for different functions, such as the left brain is better at logical analysis, and the right brain is better at spatial imagination and creative thinking. The nervous system is the "communication network" connecting the brain and various parts of the body. At work, this can be analogized to the mechanism of information transmission and execution, ensuring that decisions can be quickly and accurately transmitted to various execution departments or individuals. For example, the nervous system matches the information transmission department, but the information department transmits information of different levels. The nervous system is crucial to the human body, so it is matched to the highest level of information transmission work.

[0135] The steps of obtaining the online status of doctors, matching the text content according to the online status of doctors, and adjusting and generating primary information according to the text content and basic information are as follows:

[0136] Step S31, obtaining the online status of the doctor, and obtaining the average response interval of the doctor according to the online status.

[0137] Step S32, obtaining the patient's historical reply records, and extracting the patient's browsing speed based on the historical reply records.

[0138] Step S33, obtaining the text content of the basic information and recording it as the basic text content, and calculating the patient's browsing time according to the patient's browsing speed and the basic text content.

[0139] Step S34, determining whether the browsing time is less than the average reply interval time, if the browsing time is less than the average reply interval time, calculating the time difference between the browsing time and the average reply interval time.

[0140] Step S35, according to the preset correlation curve between the duration difference and the text content, the corresponding text content is searched and recorded as the standard text content.

[0141] Step S36, according to the text difference between the basic text content and the standard text content, add the content corresponding to the text difference to the basic information and generate primary information.

[0142] In actual use, during online consultation, due to the uncertainty of the doctor's online status, patients may sometimes have to wait, which brings a bad experience to patients. The browsing time of patients is calculated based on the browsing speed of patients and the number of words in the basic information. If the patient finishes browsing and judges that the doctor is not online based on the doctor's online status, the content of basic information can be appropriately increased. While reducing the patient's waiting time, patients can obtain more detailed information related to their condition. This not only allows patients to have a clearer understanding of their condition, but also improves the patient's good experience of consultation.

[0143] According to the text difference between the basic text content and the standard text content, the steps of adding content corresponding to the text difference to the basic information and generating primary information are specifically as follows:

[0144] Step S361, classify the basic information according to whether the description is completely specific, and obtain specific information and non-specific information respectively.

[0145] Specific information is information that can no longer be added, such as the exact name of a disease, and the name of the disease cannot be further refined.

[0146] Step S362, obtaining the amount of information in the non-specific information, and sorting the information from small to large according to the amount of information to obtain an information size sorting.

[0147] Step S363, sorting by information size, adding details to non-specific information in turn, and sequentially superimposing and recording the increased text content, which is recorded as the text added value.

[0148] Step S364, when the text addition value exceeds the text difference value, stop adding details and forming primary information.

[0149] In actual use, detailed descriptions usually contain more specific information. This information may be details about the shape, size, color, material, location, state, action, emotion, environmental background, etc. of things. The amount of information of different information is obtained according to the amount of information, and supplemented according to the difference. For example, the basic information gives information such as the name of the disease, the severity of the disease, the possible cause, and the treatment plan. Among them, the treatment plan recommends the least amount of information for gastroscopy, which can supplement the details of gastroscopy, such as preparation before the examination, the steps of gastroscopy, etc. In this way, patients can be fully prepared before going to the hospital. They cannot eat before gastroscopy. If the patient does not know, it will increase the number of visits to the hospital. Therefore, patients can learn more details while waiting for the doctor and reduce the steps of searching after the end. First, add and supplement the least information, and add in accordance with the amount of information in turn until it exceeds the text difference. If it exceeds too much, it will become that the doctor waits for the patient for too long.

[0150] Obtain the patient's historical browsing history, obtain the focus of the patient's attention information based on the historical browsing history, and adjust the steps of generating intermediate information based on the focus and primary information, specifically:

[0151] Step S41, obtaining the patient's historical browsing records and extracting the browsing duration of the patient on different pages.

[0152] Step S42, extracting the information contents corresponding to the browsing dwelling time and obtaining the information content types.

[0153] Step S43, forming a type percentage according to the ratio of the browsing dwell time corresponding to the information content type to the total browsing dwell time.

[0154] Step S44, adjusting the percentages of different types of content in the primary information according to the type percentages, and generating intermediate information after the percentages are consistent with the type percentages.

[0155] In actual use, patients focus on different aspects of their illnesses. Some patients focus on treatment plans, while others focus on the severity of their illnesses. According to the patient's past browsing time, the focus of the patient's attention is determined, and the focus of the patient's attention is explained in more detail, while the focus that the patient is less concerned about can be explained less. For example, if the patient is more concerned about the severity of the illness, the length of treatment time and the degree of damage to the body can be added to the severity of the illness. If the patient is less concerned about the treatment plan, it can be described in a more concise way, reducing the content in this regard.

[0156] A system for processing online medical consultation information based on big data, by applying the above-mentioned method for processing online medical consultation information based on big data, comprises:

[0157] The patient information module obtains the medical condition information and operation information submitted by the patient, organizes the valid information in the medical condition information and records it as patient information.

[0158] The basic information module obtains the corresponding doctor's diagnosis information based on the patient information, and converts the doctor's diagnosis information to obtain the basic information.

[0159] The primary information module obtains the online status of doctors, matches the text content according to the online status of doctors, and generates primary information based on the text content and basic information.

[0160] The intermediate information module obtains the patient's historical browsing records, obtains the focus of the patient's attention based on the historical browsing records, and generates intermediate information based on the focus and primary information.

[0161] The advanced information module obtains the patient's emotions based on the patient information analysis, generates advanced information based on the patient's emotions and primary information, and pushes the advanced information to the patient.

[0162] In actual use, the information submitted by the patient is screened to obtain effective information, and then the diagnostic information is converted and processed according to the actual situation of the diagnosis information given by the doctor to obtain basic information that is easier to understand. Combined with the doctor's online status, the text content of the basic information is adjusted, and the focus of the patient's attention is obtained according to the patient's historical browsing history. The proportion of different information content is adjusted, and finally the soothing words in the information are adjusted according to the patient's emotions, and advanced information is generated and pushed to the patient. Effectively reduce the communication barriers caused by online consultations and improve the patient's online consultation experience.

[0163] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for processing online medical consultation information based on big data, characterized in that: The following steps are involved: Obtain the medical condition information and operation information submitted by the patient, organize the valid information in the medical condition information and record it as patient information; Obtain corresponding doctor's diagnosis information according to the patient information, and convert the doctor's diagnosis information to obtain basic information; Obtain the online status of doctors, match the text content according to the online status of doctors, and adjust and generate primary information according to the text content and basic information; Obtain the patient's historical browsing history, obtain the focus of the patient's attention based on the historical browsing history, and adjust and generate intermediate information based on the focus and primary information; The patient's emotions are obtained based on the patient information analysis, and advanced information is generated based on the patient's emotions and primary information, and the advanced information is pushed to the patient.

2. A method for processing online medical consultation information based on big data according to claim 1, characterized in that: The steps of obtaining the condition information and operation information submitted by the patient, sorting out the valid information in the condition information and recording it as the patient information, are specifically as follows: Obtain the medical condition information and operation information submitted by the patient, filter out the medical condition information that is completely irrelevant to the condition, and obtain the basic information; Find the corresponding disease type according to the basic information and record it as the basic disease type; Obtain the average timeliness of basic information corresponding to basic disease types, extract the real-time timeliness of basic information, filter out basic information whose real-time timeliness exceeds the average timeliness and form effective information; Determine whether the valid information is image information. If the valid information is image information, extract the clarity of the image information as the information fuzziness; If the valid information is not image information, the text information of the valid information is obtained and recorded as the patient text information, and the information ambiguity is obtained according to the patient text information; Set the fuzziness threshold, filter out the valid information that exceeds the fuzziness threshold and record it as patient information.

3. The method for processing online medical consultation information based on big data according to claim 2, characterized in that: If the valid information is not image information, the text information of the valid information is obtained and recorded as the patient text information, and the step of obtaining the information ambiguity according to the patient text information is specifically as follows: If the valid information is not image information, the valid information is extracted and converted into text information and recorded as patient text information; Count the average operation time AP of all information in the patient operation information; Acquire operation information corresponding to the patient text information, wherein the operation information includes operation duration AC and operation modification frequency AX; Extract the descriptive words of the patient's text information, sort out the proportion of fuzzy words in the descriptive words and record it as the fuzzy word proportion AM; Obtain the criticality AG of the patient's text information in the basic disease type, according to the fuzzy correlation function The information ambiguity AH is calculated, where a1, a2, a3, and a4 are proportional factors and are greater than 0.

4. The method for processing online medical consultation information based on big data according to claim 3, characterized in that: The step of obtaining corresponding doctor's diagnosis information according to patient information and converting the doctor's diagnosis information to obtain basic information is specifically as follows: Obtaining corresponding doctor's diagnosis information according to the patient information, and determining whether the doctor's diagnosis information has corresponding image information; If the doctor's diagnosis information has corresponding image information, the human body part displayed in the image information is extracted and recorded as the displayed part; It is determined whether the patient can directly observe the display part. If the patient can directly observe the display part, the doctor's diagnosis information is converted into image information and recorded as basic information; If the patient cannot directly observe the display part, the doctor's diagnosis information is converted into text information and recorded as the doctor's text information; If the doctor's diagnosis information has no corresponding image information, the doctor's diagnosis information is converted into text information and recorded as doctor's text information; After processing the doctor's text information, basic information is obtained.

5. The method for processing online medical consultation information based on big data according to claim 4, characterized in that: The step of obtaining basic information after processing the doctor's text information is specifically as follows: Extract the disease type from the doctor's diagnosis information and record it as the reference disease type; Determine whether the patient understands the reference condition type. If the patient understands the reference condition type, the doctor's text information is not processed to obtain basic information; If the patient does not know the reference disease type, the patient's work experience is obtained, and the patient's work industry is extracted based on the work experience; Match each part of the human body to each part of the work in the industry, extract the part corresponding to the reference disease type and record it as the reference part; Find the part of the work corresponding to the reference part and record it as the reference work, obtain the workflow of the reference work and record it as the reference workflow; The workflow of the reference part is obtained and recorded as the part workflow, and the reference workflow is matched with the part workflow one by one to obtain a process comparison table; Extract the descriptive meaning of the doctor's text information and find the part workflow corresponding to the descriptive meaning; According to the process comparison table, the part workflow is converted into the corresponding reference workflow, forming an example and outputting basic information.

6. A method for processing online medical consultation information based on big data according to claim 5, characterized in that: The step of matching each part of the human body with each part of the work in the work industry, extracting the part corresponding to the reference disease type and recording it as the reference part, is specifically as follows: Obtain the main functions of each part of the human body, and obtain the higher-level functional information of the parts after performing upper-level generalization processing on the main functions; Obtain the main functions of each part of the work in the work industry, and obtain the upper-level function information of some parts of the work after summarizing the main functions; Find the partial work whose upper function information is consistent with the upper function information of the part and obtain the pending partial work; Obtain the criticality of the parts in the operation of the human body, and find out the importance of the pending parts of work in the work industry; Find the pending partial work corresponding to the criticality and importance to obtain the partial work corresponding to the human body part; Find the corresponding parts of the work for all parts, and achieve a one-to-one correspondence between various parts of the human body and various parts of the work in the work industry.

7. A method for processing online medical consultation information based on big data according to claim 6, characterized in that: The steps of obtaining the online status of doctors, matching the text content according to the online status of doctors, and adjusting and generating primary information according to the text content and basic information are specifically as follows: Obtain the online status of doctors and obtain the average response interval of doctors based on the online status; Obtain the patient's historical response records, and extract the patient's browsing speed based on the historical response records; The text content of the basic information is obtained and recorded as the basic text content, and the patient's browsing time is calculated according to the patient's browsing speed and the basic text content; Determine whether the browsing time is less than the average reply interval time. If the browsing time is less than the average reply interval time, calculate the difference between the browsing time and the average reply interval time. According to the preset correlation curve between the time difference and the text content, the corresponding text content is found and recorded as the standard text content; According to the text difference between the basic text content and the standard text content, the content corresponding to the text difference is added to the basic information to generate primary information.

8. The method for processing online medical consultation information based on big data according to claim 7, characterized in that: The step of adding content corresponding to the text difference value to the basic information and generating primary information according to the text difference value between the basic text content and the standard text content is specifically as follows: The basic information is classified according to whether the description is completely specific, and specific information and non-specific information are obtained respectively; Obtain the amount of information in the non-specific information, sort it from small to large according to the amount of information, and obtain the information size sorting; Sort by information size, add details to non-specific information in turn, and add and record the added text content in turn, which is recorded as the text added value; When the text addition value exceeds the text difference value, stop adding details and form primary information.

9. The method for processing online medical consultation information based on big data according to claim 8, characterized in that: The steps of obtaining the patient's historical browsing records, obtaining the focus of the patient's attention information according to the historical browsing records, and adjusting and generating the intermediate information according to the focus and the primary information are specifically as follows: Obtain the patient's browsing history and extract the patient's browsing duration on different pages; Extract the information contents corresponding to the browsing duration respectively and obtain the information content types; The type percentage is formed based on the ratio of the browsing dwell time corresponding to the information content type to the total browsing dwell time; The percentage of different types of content in the primary information is adjusted according to the type percentage, and the intermediate information is generated after it is consistent with the type percentage.

10. A system for processing online medical consultation information based on big data, characterized in that: By applying a method for processing online medical consultation information based on big data as described in any one of claims 1 to 9, the method comprises: The patient information module obtains the medical condition information and operation information submitted by the patient, sorts out the valid information in the medical condition information and records it as the patient information; The basic information module obtains the corresponding doctor's diagnosis information according to the patient information, and converts the doctor's diagnosis information to obtain the basic information; The primary information module obtains the online status of doctors, matches the text content according to the online status of doctors, and generates primary information according to the text content and basic information; The intermediate information module obtains the patient's historical browsing records, obtains the focus of the patient's attention based on the historical browsing records, and generates intermediate information based on the focus and primary information; The advanced information module obtains the patient's emotions based on the patient information analysis, generates advanced information based on the patient's emotions and primary information, and pushes the advanced information to the patient.

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